The forthcoming wave of Web 3.0 goes far beyond the initial use case of cryptocurrencies. Through the richness of interactions now possible and the global scope of counter-parties available, Web 3.0 will cryptographically connect data from individuals, corporations and machines, with efficient machine learning algorithms, leading to the rise of fundamentally new markets and associated business models.
The future impact of Web 3.0 makes undeniable sense, but the question remains, which business models will crack the code to provide lasting and sustainable value in today’s economy?
We will dive into native business models that have been and will be enabled by Web 3.0, while first briefly touching upon the quick-forgotten but often arduous journeys leading to the unexpected & unpredictable successful business models that emerged in Web 2.0.
To set the scene anecdotally for Web 2.0’s business model discovery process, let us not forget the journey that Google went through from their launch in 1998 to 2002 before going public in 2004:
After struggling for 4 years, a single small modification to their business model launched Google into orbit to become one of the worlds most valuable companies.
The earliest iterations of online content merely involved the digitisation of existing newspapers and phone books … and yet, we’ve now seen Roma (Alfonso Cuarón) receive 10 Academy Awards Nominations for a movie distributed via the subscription streaming giant Netflix.
Amazon started as an online bookstore that nobody believed could become profitable … and yet, it is now the behemoth of marketplaces covering anything from gardening equipment to healthy food to cloud infrastructure.
Open source software development started off with hobbyists and an idealist view that software should be a freely-accessible common good … and yet, the entire internet runs on open source software today, creating $400b of economic value a year and Github was acquired by Microsoft for $7.5b while Red Hat makes $3.4b in yearly revenues providing services for Linux.
In the early days of Web 2.0, it might have been inconceivable that after massively spending on proprietary infrastructure one could deliver business software via a browser and become economically viable … and yet, today the large majority of B2B businesses run on SaaS models.
It was hard to believe that anyone would be willing to climb into a stranger’s car or rent out their couch to travellers … and yet, Uber and AirBnB have become the largest taxi operator and accommodation providers in the world, without owning any cars or properties.
While Google and Facebook might have gone into hyper-growth early on, they didn’t have a clear plan for revenue generation for the first half of their existence … and yet, the advertising model turned out to fit them almost too well, and they now generate 58% of the global digital advertising revenues ($111B in 2018) which has become the dominant business model of Web 2.0.
Taking a look at Web 3.0 over the past 10 years, initial business models tend not to be repeatable or scalable, or simply try to replicate Web 2.0 models. We are convinced that while there is some scepticism about their viability, the continuous experimentation by some of the smartest builders will lead to incredibly valuable models being built over the coming years.
By exploring both the more established and the more experimental Web 3.0 business models, we aim to understand how some of them will accrue value over the coming years.
Bitcoin came first. Proof of Work coupled with Nakamoto Consensus created the first Byzantine Fault Tolerant & fully open peer to peer network. Its intrinsic business model relies on its native asset: BTC — a provable scarce digital token paid out to miners as block rewards. Others, including Ethereum, Monero and ZCash, have followed down this path, issuing ETH, XMR and ZEC.
These native assets are necessary for the functioning of the network and derive their value from the security they provide: by providing a high enough incentive for honest miners to provide hashing power, the cost for malicious actors to perform an attack grows alongside the price of the native asset, and in turn, the added security drives further demand for the currency, further increasing its price and value. The value accrued in these native assets has been analysed & quantified at length.
Some of the earliest companies that formed around crypto networks had a single mission: make their respective networks more successful & valuable. Their resultant business model can be condensed to “increase their native asset treasury; build the ecosystem”. Blockstream, acting as one of the largest maintainers of Bitcoin Core, relies on creating value from its balance sheet of BTC. Equally, ConsenSys has grown to a thousand employees building critical infrastructure for the Ethereum ecosystem, with the purpose of increasing the value of the ETH it holds.
While this perfectly aligns the companies with the networks, the model is hard to replicate beyond the first handful of companies: amassing a meaningful enough balance of native assets becomes impossible after a while … and the blood, toil, tears and sweat of launching & sustaining a company cannot be justified without a large enough stake for exponential returns. As an illustration, it wouldn’t be rational for any business other than a central bank — i.e. a US remittance provider — to base their business purely on holding large sums of USD while working on making the US economy more successful.
The subsequent generation of business models focused on building the financial infrastructure for these native assets: exchanges, custodians & derivatives providers. They were all built with a simple business objective — providing services for users interested in speculating on these volatile assets. While the likes of Coinbase, Bitstamp & Bitmex have grown into billion-dollar companies, they do not have a fully monopolistic nature: they provide convenience & enhance the value of their underlying networks. The open & permissionless nature of the underlying networks makes it impossible for companies to lock in a monopolistic position by virtue of providing “exclusive access”, but their liquidity and brands provide defensible moats over time.
With The Rise of the Token Sale, a new wave of projects in the blockchain space based their business models on payment tokens within networks: often creating two sided marketplaces, and enforcing the use of a native token for any payments made. The assumptions are that as the network’s economy would grow, the demand for the limited native payment token would increase, which would lead to an increase in value of the token. While the value accrual of such a token model is debated, the increased friction for the user is clear — what could have been paid in ETH or DAI, now requires additional exchanges on both sides of a transaction. While this model was widely used during the 2017 token mania, its friction-inducing characteristics have rapidly removed it from the forefront of development over the past 9 months.
Revenue generating communities, companies and projects with a token might not always be able to pass the profits on to the token holders in a direct manner. A model that garnered a lot of interest as one of the characteristics of the Binance (BNB) and MakerDAO (MKR) tokens was the idea of buybacks / token burns. As revenues flow into the project (from trading fees for Binance and from stability fees for MakerDAO), native tokens are bought back from the public market and burned, resulting in a decrease of the supply of tokens, which should lead to an increase in price. It’s worth exploring Arjun Balaji’s evaluation (The Block), in which he argues the Binance token burning mechanism doesn’t actually result in the equivalent of an equity buyback: as there are no dividends paid out at all, the “earning per token” remains at $0.
One of the business models for crypto-networks that we are seeing ‘hold water’ is the work token: a model that focuses exclusively on the revenue generating supply side of a network in order to reduce friction for users. Some good examples include Augur’s REP and Keep Network’s KEEP tokens. A work token model operates similarly to classic taxi medallions, as it requires service providers to stake / bond a certain amount of native tokens in exchange for the right to provide profitable work to the network. One of the most powerful aspects of the work token model is the ability to incentivise actors with both carrot (rewards for the work) & stick (stake that can be slashed). Beyond providing security to the network by incentivising the service providers to execute honest work (as they have locked skin in the game denominated in the work token), they can also be evaluated by predictable future cash-flows to the collective of service providers (we have previously explored the benefits and valuation methods for such tokens in this blog). In brief, such tokens should be valued based of the future expected cash flows attributable to all the service providers in the network, which can be modelled out based on assumptions on pricing and usage of the network.
A wide array of other models are being explored and worth touching upon:
With this wealth of new business models arising and being explored, it becomes clear that while there is still room for traditional venture capital, the role of the investor and of capital itself is evolving. The capital itself morphs into a native asset within the network which has a specific role to fulfil. From passive network participation to bootstrap networks post financial investment (e.g. computational work or liquidity provision) to direct injections of subjective work into the networks (e.g. governance or CDP risk evaluation), investors will have to reposition themselves for this new organisational mode driven by trust minimised decentralised networks.
When looking back, we realise Web 1.0 & Web 2.0 took exhaustive experimentation to find the appropriate business models, which have created the tech titans of today. We are not ignoring the fact that Web 3.0 will have to go on an equally arduous journey of iterations, but once we find adequate business models, they will be incredibly powerful: in trust minimised settings, both individuals and enterprises will be enabled to interact on a whole new scale without relying on rent-seeking intermediaries.
Today we see 1000s of incredibly talented teams pushing forward implementations of some of these models or discovering completely new viable business models. As the models might not fit the traditional frameworks, investors might have to adapt by taking on new roles and provide work and capital (a journey we have already started at Fabric Ventures), but as long as we can see predictable and rational value accrual, it makes sense to double down, as every day the execution risk is getting smaller and smaller
Source : https://medium.com/fabric-ventures/which-new-business-models-will-be-unleashed-by-web-3-0-4e67c17dbd10
TL;DR – those discussing what should be appropriate regulatory benchmarks for API performance and availability under PSD2 are missing a strategic opportunity. Any bank that simply focusses on minimum, mandatory product will rule itself out of commercial agreements with those relying parties who have the wherewithal to consume commercial APIs at scale.
As March approaches, those financial institutions in the UK and Ireland impacted by PSD2 are focussed on readiness for full implementation. The Open Banking Implementation Entity (OBIE) has been consulting on Operational Guidelineswhich give colour to the regulatory requirements found in the Directive and Regulatory Technical Standards which support it. The areas covered are not unique to the UK, and whilst they are part of an OBIE-specific attestation process, the guidelines could prove useful to any ASPSP impacted by PSD2.
The EBA at guidelines 2.2-4 are clear on the obligations for ASPSPs. These are supplemented by the RTS – ” [ASPSPs must] ensure that the dedicated interface offers at all times the same level of availability and performance, including support, as the interfaces made available to the payment service user for directly accessing its payment account online…” and “…define transparent key performance indicators and service level targets, at least as stringent as those set for the interface used by their payment service users both in terms of availability and of data provided in accordance with Article 36″ (RTS Arts. 32(1) and (2)).
This places the market in a quandary – it is extremely difficult to compare, even at a theoretical level, the performance of two interfaces where one (PSU) is designed for human interaction and the other (API) for machine. Some suggested during the EBA’s consultation period that a more appropriate comparison might be between the APIs which support the PSU interface and those delivered in response to PSD2. Those in the game of reverse engineering confirm that there is broad comparability between the functions these support – unfortunately this proved too much technical detail for the EBA.
To fill the gap, OB surveyed the developers, reviewed those existing APIs already delivered by financial institutions, and settled on an average of 99% availability (c.22hrs downtime per quarter) and 1000 m/s per 1MB of payload response time (this is a short summary and more detail can be read on the same). A quick review of the API Performance page OB publish will show that, with average availability of 96.34% across the brands in November, and only Bank of Scotland, Lloyds and the HSBC brands achieving >99% availability, there is a long way to go before this target is met, made no easier by a significant amount of change to platforms as their functional scope expands over the next 6-8 months. This will also been in the face of increasing demand volumes, as those organisations which currently rely on screen scraping for access to data begin to transfer their integrations onto APIs. In short, ASPSPs are facing a perfect storm to achieve these goals.
At para 2.3.1 of their guidelines, the OBIE expands on the EBA’s reporting guidelines, and provides a useful template for this purpose, but this introduces a conundrum. All of the data published to date has been the banks reporting on themselves – i.e. the technical solutions to generate this data sit inside their domains, so quite apart from the obvious issue of self-reporting, there have already been clear instances where services haven’t been functioning correctly, and the bank in question simply hasn’t known this to be the case until so informed by a TPP. One of the larger banks in the UK recently misconfigured a load balancer to the effect that 50% of the traffic it received was misdirected and received no response, but without its knowledge. A clear case of downtime that almost certainly went unreported – if an API call goes unacknowledged in the woods, does anyone care?
Banks have a challenge, in that risk and compliance departments typically baulk at any services they own being placed in the cloud, or indeed anywhere outside their physical infrastructure. This is absolutely what is required for their support teams to have a true understanding of how their platforms are functioning, and to generate reliable data for their regulatory reporting requirements.
[During week commencing 21st Jan, the Market Data Initiative will announce a free/open service to solve some of these issues. This platform monitors the performance and availability of API platforms using donated consents, with the aim of establishing a clear, independent view of how the market is performing, without prejudicial comment or reference to benchmarks. Watch this space for more on that.]
For any TPP seeking investment, where their business model necessitates consuming open APIs at scale, one of the key questions they’re likely to face is how reliable these services are, and what remedies are available in the event of non-performance. In the regulatory space, some of this information is available (see above) but is hardly transparent or independently produced, and even with those caveats does not currently make for happy reading. For remedy, TPPs are reliant on regulators and a quarterly reporting cycle for the discovery of issues. Even in the event that the FCA decided to take action, the most significant step they could take would be to instruct and ASPSP to implement a fall-back interface, and given that they would have a period of weeks to build this, it is likely that any relying party’s business would have suffered significant detriment before it could even start testing such a facility. The consequence of this framework is that, for the open APIs, performance, availability and the transparency of information will have to improve dramatically before any commercial services rely on them.
Source : https://www.linkedin.com/pulse/api-metrics-status-regulatory-requirement-strategic-john?trk=portfolio_article-card_title
At a recent KPMG Robotic Innovations event, Futurist and friend Gerd Leonhard delivered a keynote titled “The Digital Transformation of Business and Society: Challenges and Opportunities by 2020”. I highly recommend viewing the Video of his presentation. As Gerd describes, he is a Futurist focused on foresight and observations — not predicting the future. We are at a point in history where every company needs a Gerd Leonhard. For many of the reasons presented in the video, future thinking is rapidly growing in importance. As Gerd so rightly points out, we are still vastly under-estimating the sheer velocity of change.
With regard to future thinking, Gerd used my future scenario slide to describe both the exponential and combinatorial nature of future scenarios — not only do we need to think exponentially, but we also need to think in a combinatorial manner. Gerd mentioned Tesla as a company that really knows how to do this.
He then described our current pivot point of exponential change: a point in history where humanity will change more in the next twenty years than in the previous 300. With that as a backdrop, he encouraged the audience to look five years into the future and spend 3 to 5% of their time focused on foresight. He quoted Peter Drucker (“In times of change the greatest danger is to act with yesterday’s logic”) and stated that leaders must shift from a focus on what is, to a focus on what could be. Gerd added that “wait and see” means “wait and die” (love that by the way). He urged leaders to focus on 2020 and build a plan to participate in that future, emphasizing the question is no longer what-if, but what-when. We are entering an era where the impossible is doable, and the headline for that era is: exponential, convergent, combinatorial, and inter-dependent — words that should be a key part of the leadership lexicon going forward. Here are some snapshots from his presentation:
Source: B. Joseph Pine II and James Gilmore: The Experience Economy
Gerd then summarized the session as follows:
The future is exponential, combinatorial, and interdependent: the sooner we can adjust our thinking (lateral) the better we will be at designing our future.
My take: Gerd hits on a key point. Leaders must think differently. There is very little in a leader’s collective experience that can guide them through the type of change ahead — it requires us all to think differently
When looking at AI, consider trying IA first (intelligent assistance / augmentation).
My take: These considerations allow us to create the future in a way that avoids unintended consequences. Technology as a supplement, not a replacement
Efficiency and cost reduction based on automation, AI/IA and Robotization are good stories but not the final destination: we need to go beyond the 7-ations and inevitable abundance to create new value that cannot be easily automated.
My take: Future thinking is critical for us to be effective here. We have to have a sense as to where all of this is heading, if we are to effectively create new sources of value
We won’t just need better algorithms — we also need stronger humarithms i.e. values, ethics, standards, principles and social contracts.
My take: Gerd is an evangelist for creating our future in a way that avoids hellish outcomes — and kudos to him for being that voice
“The best way to predict the future is to create it” (Alan Kay).
My Take: our context when we think about the future puts it years away, and that is just not the case anymore. What we think will take ten years is likely to happen in two. We can’t create the future if we don’t focus on it through an exponential lens
Source : https://medium.com/@frankdiana/digital-transformation-of-business-and-society-5d9286e39dbf
Naval Ravikant recently shared this thought:
“The dirty secrets of blockchains: they don’t scale (yet), aren’t really decentralized, distribute wealth poorly, lack killer apps, and run on a controlled Internet.”
In this post, I want to dive into his fourth observation that blockchains “lack killer apps” and understand just how far away we are to real applications (not tokens, not store of value, etc.) being built on top of blockchains.
Thanks to Dappradar, I was able to analyze the top decentralized applications (DApps) built on top of Ethereum, the largest decentralized application platform. My research is focused on live public DApp’s which are deployed and usable today. This does not include any future or potential applications not deployed yet.
If you look at a broad overview of the 312 DApps created, the main broad categories are:
I. Decentralized Exchanges
II. Games (Largely collectible type games, excluding casino/games of chance)
III. Casino Applications
IV. Other (we’ll revisit this category later)
On closer examination, it becomes clear only a few individual DApps make up the majority of transactions within their respective category:
Diving into the “Other” category, the largest individual DApps in this category are primarily pyramid schemes: PoWH 3D, PoWM, PoWL, LockedIn, etc. (*Please exercise caution, all of these projects are actual pyramid schemes.)
These top DApps are all still very small relative to traditional consumer web and mobile applications.
Further trends emerge on closer inspection of the transactions of DApps tracked here:
Where we are and what it means for protocols and the ecosystem:
After looking through the data, my personal takeaways are:
What kind of DApps do you think we as a community should be building? Would love to hear your takeaways and thoughts about the state of DApps, feel free to comment below or tweet @mccannatron.
Also, if there are any DApps or UI/UX tools I should be paying attention to, let me know — I would love to check them out.
With all of the noise surrounding bitcoin and its underlying technology, blockchain, it’s often difficult to separate real blockchain articles from those just looking for clicks.
Here’s a list, in no particular order, of articles and whitepapers written by the people actively involved in developing this new technology. The resources below range all the way back from the 1980s to today.